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This document is an editorial from the Journal of Maps, discussing the importance of community support and collaboration within the academic community. The editor announces the winner of the 2023 Best Map Competition, which was awarded to a team for their Geological Map of South America in Google Earth. The document also provides a summary of the journal's productivity in 2023, with 133 articles published. The Impact Factor slightly dropped to 2.2 but is still higher than the previous year. The number of downloads for articles in open access journals increased by nearly 10% to 458,576. The top five cited and downloaded articles are listed, with the Best Map prizewinner receiving 10,177 downloads. The author expresses gratitude to the academic community, authors, reviewers, and editorial board members for their contributions to the journal. Additionally, the document is a list of references for various articles published in the Journal of Maps, covering topics such as geological mapping, geospatial analysis, geomorphology, and cartography. The authors are affiliated with universities and research institutions from around the world. The articles are available under an Open Access license, allowing for non-commercial use and distribution. [Extracted from the article]

Journal of Maps, 17 (1), 1 - 8. https://doi.org/10.1080/17445647.2021.1875063 4 Chelli, A., Bordoni, M., Cappadonia, C., Pepe, G., Rotigliano, E., & Smith, M. (2021). Best Map Award For 2021 the "Best Map"' was judged by the formal awards panel comprised of myself, Dr Dick Berg, Dr Bernhard Jenny, Mr Mike Shand, and Professor Nigel Walford (and this section reflects our combined comments). Journal of Maps, 17 (1), 145 - 153. https://doi.org/10.1080/17445647.2021.1996475 2 Brandolini, P., Del Monte, M., Faccini, F., Cattoor, B., Zwoli'nski, Z., & Smith, M. (2021). Journal of Maps, 14 (2), 257 - 268. https://doi.org/10.1080/17445647.2018.1456490 9 Fioraso, G., Monegato, G., Balestro, G., Hajdas, I., & Baggio, P. (2021). [Extracted from the article]

Geomorphological mapping can be described as a group of techniques employed to systematically record the morphology of the ground, landforms, landscape-forming processes and materials that constitute the surface of the Earth (Griffiths et al., [13]; Lee, [16]). In the paper by [10], the "geo-historical" map is described as a synthesis of historical and geomorphological data for landslides and landslide occurrence in the Ceno Torrent valley (Northern Apennines, Italy). The knowledge of flood-induced ground effects and flood-water dynamics is underlined as a crucial issue for hydro-geomorphic hazard assessment and mapping. This special issue presents contributions about the methodologies and tools for representing and mapping geomorphological hazards, with a focus upon hazard and risk classification and tools for land planning, risk reduction and mitigation. [Extracted from the article]

[5] map raw material catchments in central Europe based on the origin of raw material and archaeological site distributions. Research on our own specie's history with the spread of humans across the globe, throughout variable landscapes which incorporate past human-environment interactions, is an in-depth interdisciplinary field of science. [2] show how landscape archaeology and spatial statistics reveal the landscape heritage in the Northern Apennines and anthropogenic modifications through population changes in historic times. [Extracted from the article]

Abstract: Mapping or “delimiting” landforms is one of geomorphology's primary tools. Computer-based techniques such as land-surface segmentation allow the emulation of the process of manual landform delineation. Land-surface segmentation exhaustively subdivides a digital elevation model (DEM) into morphometrically-homogeneous irregularly-shaped regions, called terrain segments. Terrain segments can be created from various land-surface parameters (LSP) at multiple scales, and may therefore potentially correspond to the spatial extents of landforms such as drumlins. However, this depends on the segmentation algorithm, the parameterization, and the LSPs. In the present study we assess the widely used multiresolution segmentation (MRS) algorithm for its potential in providing terrain segments which delimit drumlins. Supervised testing was based on five 5-m DEMs that represented a set of 173 synthetic drumlins at random but representative positions in the same landscape. Five LSPs were tested, and four variants were computed for each LSP to assess the impact of median filtering of DEMs, and logarithmic transformation of LSPs. The testing scheme (1) employs MRS to partition each LSP exhaustively into 200 coarser scales of terrain segments by increasing the scale parameter (SP), (2) identifies the spatially best matching terrain segment for each reference drumlin, and (3) computes four segmentation accuracy metrics for quantifying the overall spatial match between drumlin segments and reference drumlins. Results of 100 tests showed that MRS tends to perform best on LSPs that are regionally derived from filtered DEMs, and then log-transformed. MRS delineated 97% of the detected drumlins at SP values between 1 and 50. Drumlin delimitation rates with values up to 50% are in line with the success of manual interpretations. Synthetic DEMs are well-suited for assessing landform quantification methods such as MRS, since subjectivity in the reference data is avoided which increases the reliability, validity and applicability of results. [Copyright &y& Elsevier]

Geomorphology (the study of Earth surface landforms) is a discipline that requires effective geovisualisation in four dimensions; that is landforms in 3D space, and their change through time. For example, in alpine environments, the 3D shapes of landforms are subject to processes of glacial erosion and deposition that cause them, over time, to be created, evolve and eventually, be destroyed. Until recently, the principal visualisation in this domain was through geomorphological mapping in 2D and 3D (Smith et al, 2013). Advances in science, technology and art have changed the "landscape" of geovisualisation in geomorphology, predominantly through mixed reality environments, visual exploration (e.g. of "Big Data" through the collation of vast environmental datasets) and approaches that are complementary with the increasing global human impact. These, in turn, afford opportunities for future research and development, the topic of this submission. Recent changes, particularly technology-led, have led geovisualisation to embrace 3D and 4D approaches. Examples include virtual reality, augmented reality, innovative static space-time cube displays and the efficient handling of dynamic visualisation (animation). However, it could be argued there have been missed opportunities in visualisation in the past. Geomorphological mapping activities were and are exclusively visual, which makes them ripe for experimentation with non-visual channels such as sound, but particularly with tactile and haptic communication in the study of landforms, where shape, (textural) structure and volume are particularly important. In the case of VR, there is the acknowledged facilitation of indirect interaction with, and representation of, hazardous and extraterrestrial locations, as well as past and future time points. The future potential of VR in particular can be seen to include exploration of the property of embodiment (e.g. along the lines of Ahn et al, 2016, for coral bleaching), where users could be induced to 'feel' geomorphological process through first-person landform avatars (e.g. a retreating glacier). Through mixed reality as a whole, there is now an ambiguity with where visualisation takes place in relation to the natural geomorphological environment. While VR tries to replicate the environment in the lab, AR allows seamless visualisation in situ. AR, and in particular tangible AR instances such as sandtables, also gives direct control of physical 'toy' environments allowing for ease of experimentation (e.g. Moore et al, 2020). This has been a real advance for physical geographic education (e.g. Virtual Field Trips) and research. Fundamentally, the role of the human is inescapable in visualisation, which now has a central function of dynamic interaction as well as representation / mapping. Therefore, human-led aspects of visualisation such as cognition, usability testing and personalised maps and interfaces (through programming) are as a result open to geomorphology. The creation and adaptation of games for geomorphological education and research represents an opportunity (partially realised in the urban geographic domain) as a geovisualisation means already familiar to a large group of potential users. The natural environment now has the indelible imprint of humans, which has lent added meaning to the application of art to geomorphology. There is an increasing recognition that art can provide a humanistic alternative to scientific capture, analysis and visualisation (Tooth et al, 2019), with geomorphologists and artists collaborating on artworks that represent a world demonstrating a dominant anthropogenic signature. Geomorphologists have had limited impact in artistic renditions of landscape in the past. This is also true of landscape spatialisations (using landscape in a cognitively plausible manner to represent non-spatial data), which should communicate through our shared geomorphological knowledge (Fabrikant et al, 2010). Geomorphologists can also contribute their expertise in landform and process to virtual and game environments too. These, along with developments associated with immersive environments themselves (e.g. embodiment) and visual exploration (e.g. of Big Data), are areas of opportunity for future geomorphological geovisualisation. [ABSTRACT FROM AUTHOR]

Abstract: Metrics such as height and volume quantifying the 3D morphology of landforms are important observations that reflect and constrain Earth surface processes. Errors in such measurements are, however, poorly understood. A novel approach, using statistically valid ‘synthetic’ landscapes to quantify the errors is presented. The utility of the approach is illustrated using a case study of 184 drumlins observed in Scotland as quantified from a Digital Elevation Model (DEM) by the ‘cookie cutter’ extraction method. To create the synthetic DEMs, observed drumlins were removed from the measured DEM and replaced by elongate 3D Gaussian ones of equivalent dimensions positioned randomly with respect to the ‘noise’ (e.g. trees) and regional trends (e.g. hills) that cause the errors. Then, errors in the cookie cutter extraction method were investigated by using it to quantify these ‘synthetic’ drumlins, whose location and size is known. Thus, the approach determines which key metrics are recovered accurately. For example, mean height of 6.8m is recovered poorly at 12.5±0.6 (2σ) m, but mean volume is recovered correctly. Additionally, quantification methods can be compared: A variant on the cookie cutter using an un-tensioned spline induced about twice (×1.79) as much error. Finally, a previously reportedly statistically significant (p =0.007) difference in mean volume between sub-populations of different ages, which may reflect formational processes, is demonstrated to be only 30–50% likely to exist in reality. Critically, the synthetic DEMs are demonstrated to realistically model parameter recovery, primarily because they are still almost entirely the original landscape. Results are insensitive to the exact method used to create the synthetic DEMs, and the approach could be readily adapted to assess a variety of landforms (e.g. craters, dunes and volcanoes). [Copyright &y& Elsevier]

Surface roughness is an important geomorphological variable which has been used in the Earth and planetary sciences to infer material properties, current/past processes, and the time elapsed since formation. No single definition exists; however, within the context of geomorphometry, we use surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter's behavior at different spatial scales and data-set resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenization of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography highlighted local features and did not detect regional relief. Standard deviation of elevation correctly identified breaks of slope and was good at detecting regional relief. Standard deviation of slope (\SDslope) also correctly identified smooth sloping areas and breaks of slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks of slope, although not as strongly as \SDslope, and it is sensitive to noise and spurious data. In general, \SDslope offered good performance at a variety of scales, while the simplicity of calculation is perhaps its single greatest benefit. [ABSTRACT FROM AUTHOR]